scholarly journals Improving the classification accuracy for IR spectroscopic diagnosis of stomach and colon malignancy using non-linear spectral feature extraction methods

The Analyst ◽  
2013 ◽  
Vol 138 (14) ◽  
pp. 4076 ◽  
Author(s):  
Sanguk Lee ◽  
Kyoungok Kim ◽  
Hyeseon Lee ◽  
Chi-Hyuck Jun ◽  
Hoeil Chung ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Spectral Feature ◽  
Extraction Methods ◽  
Non Linear ◽  
Ir Spectroscopic

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

scholarly journals The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4749
Author(s):  
Shaorong Zhang ◽  
Zhibin Zhu ◽  
Benxin Zhang ◽  
Bao Feng ◽  
Tianyou Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Extraction Time ◽  
Classification Model ◽  
Discrete Wavelet ◽  
New Feature

The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

scholarly journals Vibration Feature Analysis for Gas-Insulated Switchgear Mechanical Fault Detection under Varying Current

2020 ◽  
Vol 10 (3) ◽  
pp. 944 ◽  
Author(s):  
Ying Feng ◽  
Jianwen Wu
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Extraction Methods ◽  
Mechanical Analysis ◽  
Linear Vibration ◽  
Feature Extraction Method ◽  
Gas Insulated Switchgear ◽  
Non Linear ◽  
Actual Operation ◽  
Mechanical Defect

As a key component to ensure the safe operation of the power grid, mechanical defect diagnosis technology of gas-insulated switchgear (GIS) during operation is often neglected. At present, GIS mechanical fault detection based on vibration information has not been developed. The main reason is that the excitation current is considerable but uncontrollable in the actual operation of GIS. It is difficult to eliminate the influence of excitation on the vibration amplitude and form an effective vibration feature description technology. Therefore, this paper proposes a unified feature-extraction method for GIS vibration information that reduces the influence of current amplitude for mechanical fault diagnosis. Starting from the GIS mechanical analysis, the periodicity of vibration excitation and the influence of amplitude are discussed. Then, combined with the non-linear characteristics of GIS systems and non-linear vibration theory, the multiplier frequency energy ratio (MFER) is designed to extract vibration-unified features of GIS for diagnosing the mechanical fault under different current levels. The diagnosis results of the experimental data with different feature-extraction methods show the applicability and superiority of the proposed method in the GIS’s mechanical fault-detection field based on vibration information.

Learning Advanced Brain Computer Interface Technology

2019 ◽  
Vol 15 (3) ◽  
pp. 14-27
Author(s):  
Wang Tao ◽  
Wu Linyan ◽  
Li Yanping ◽  
Gao Nuo ◽  
Zhang Weiran
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Support Vector ◽  
Wavelet Packet Analysis ◽  
Accuracy Rate ◽  
Desirable Outcome ◽  
Eeg Data ◽  
System Data

Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome.

A SEIZURE DETECTION METHOD BASED ON WELL-SOLVED NONLINEAR AND NON-STATIONARY PROBLEMS WITH ELECTROENCEPHALOGRAPHIC SIGNALS

2018 ◽  
Vol 30 (05) ◽  
pp. 1850037
Author(s):  
Xia Zhang ◽  
Haijun Chen
Keyword(s):  
Feature Extraction ◽  
Epileptic Seizure ◽  
Classification Accuracy ◽  
Seizure Detection ◽  
Extraction Methods ◽  
Epileptic Seizure Detection ◽  
Healthy State ◽  
Cepstral Coefficients ◽  
The Moment ◽  
Stationary Problems

The main focus of this paper is to solve the nonlinear and non-stationary problems in electroencephalographic (EEG) signals, which has been solved by the proposed method by using convolutional neural networks (CNN) as the classifiers and assembling Local Mean Decomposition (LMD) and cepstral coefficients as the feature extraction methods to achieve epileptic seizure detection with signal analysis and processing. In this proposed method, LMD and cepstral coefficients have been employed to solve the nonlinear and non-stationary problems in feature extraction and infusion, and then, the feature can be employed to feed to the recognition engine named CNN, and finally, the epileptic seizure detection can be achieved by this step. Publicly available EEG database from the University of Bonn (UoB), Germany had been used to verify the effectiveness and robustness of this proposed method on feature extraction. The complete dataset of total 7960 EEG segments, three recognition problems marked as AB versus CD versus E, the average classification accuracy of these segments can be generally obtained as highly as 99.84%, the maximal classification accuracy is 99.87%, and the lowest recognition accuracy is 98.74%. To the best of our knowledge, the excellent performance of the proposed method has shown that this method can be employed to track the patient’s healthy state and monitor the moment of epilepsy seizure.

scholarly journals An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4495 ◽  
Author(s):  
Theekshana Dissanayake ◽  
Yasitha Rajapaksha ◽  
Roshan Ragel ◽  
Isuru Nawinne
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Emotion Recognition ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Learning Approach ◽  
Human Emotion ◽  
Different Types ◽  
Human Emotions

Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.

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